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[2602.15895] Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion
Summary
The paper presents CogitoRAG, a novel Retrieval-Augmented Generation framework that enhances semantic integrity and reasoning in language models by mimicking human cognitive memory processes.
Why It Matters
As AI systems increasingly rely on external knowledge to improve accuracy, understanding and integrating human-like cognitive processes can significantly enhance the performance of language models. This research addresses the limitations of existing frameworks, offering a promising approach to mitigate issues like hallucinations in AI-generated content.
Key Takeaways
- CogitoRAG simulates human cognitive memory to improve language model performance.
- The framework utilizes a multi-dimensional knowledge graph for enhanced semantic retrieval.
- Experimental results show CogitoRAG outperforms existing RAG methods in complex query handling.
Computer Science > Computation and Language arXiv:2602.15895 (cs) [Submitted on 11 Feb 2026] Title:Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion Authors:Pengcheng Zhou, Haochen Li, Zhiqiang Nie, JiaLe Chen, Qing Gong, Weizhen Zhang, Chun Yu View a PDF of the paper titled Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion, by Pengcheng Zhou and 6 other authors View PDF HTML (experimental) Abstract:Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge. However, the inherent discrete representation of text in existing frameworks often results in a loss of semantic integrity, leading to retrieval deviations. Inspired by the human episodic memory mechanism, we propose CogitoRAG, a RAG framework that simulates human cognitive memory processes. The core of this framework lies in the extraction and evolution of the Semantic Gist. During the offline indexing stage, CogitoRAG first deduces unstructured corpora into gist memory corpora, which are then transformed into a multi-dimensional knowledge graph integrating entities, relational facts, and memory nodes. In the online retrieval stage, the framework handles complex queries via Query Decomposition Module that breaks them into comprehensive sub-queries, mimicking the cognitive decomposition humans employ for complex information. Subsequently, Entity Diffusion Module performs assoc...
